Our KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an autonomous, domain-independent, and scalable manner. In its first major run, KNOWITALL extracted over 50,000 facts with high precision, but suggested a challenge: How can we improve KNOWITALL’s recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Rule Learning learns domain-specific extraction rules. Subclass Extraction automatically identifies sub-classes in order to boost recall. List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL’s domain-independent methods, no hand-labeled training examples are required. Experiments show the relative coverage of each method and demonstrate their synergy. In concert, our methods gave KNOWITALL a 4-fold to 19-fold increase in recall, while maintaining high precision, and discovered 10,300 cities missing from the Tipster Gazetteer.

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